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We evaluated whether model explanations could efficiently detect bias in image classification by highlighting discriminating features, thereby removing the reliance on sensitive attributes for fairness calculations.
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Huang, G. B., Mattar, M., Berg, T., and Learned-Miller, E · 2008
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Google photos tags two african-americans as gorillas through facial recognition software
Zhang, M · 2012
Earlier work this paper cites.
Mammogram image quality as a potential contributor to disparities in breastcancer stage at diagnosis: an observational study
Rauscher, G. H., Conant, E. F., Khan, J. A., and Berbaum, M. L · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
Earlier work this paper cites.
Auditing algorithms: Research methods for detecting discrimination on internet platforms
Sandvig, C., Hamilton, K., Karahalios, K., and Langbort, C · 2014
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
Cited alongside, same era.
The case for process fairness in learning: Feature selection for fair decision making
Grgic-Hlaca, N., Zafar, M. B., Gummadi, K. P., and Weller, A · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
Cited alongside, same era.
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., and Sayres, R · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
Cited alongside, same era.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Zafar, M. B., Valera, I., Gomez Rodriguez, M., and Gummadi, K. P · 2017
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T · 2018
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Fairness in proprietary image tagging algorithms: A cross-platform audit on people images
Kyriakou, K., Barlas, P., Kleanthous, S., and Otterbacher, J · 2019
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Human evaluation of models built for interpretability
Lage, I., Chen, E., He, J., Narayanan, M., Kim, B., Gershman, S. J., and Doshi-Velez, F · 2019
Later among the works it cites.
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